A regression framework to uncover pleiotropy in large-scale electronic health record data.

ABSTRACT:

Objective

Pleiotropy, where 1 genetic locus affects multiple phenotypes, can offer significant insights in understanding the complex genotype-phenotype relationship. Although individual genotype-phenotype associations have been thoroughly explored, seemingly unrelated phenotypes can be connected genetically through common pleiotropic loci or genes. However, current analyses of pleiotropy have been challenged by both methodologic limitations and a lack of available suitable data sources.

Materials and methods

In this study, we propose to utilize a new regression framework, reduced rank regression, to simultaneously analyze multiple phenotypes and genotypes to detect pleiotropic effects. We used a large-scale biobank linked electronic health record data from the Penn Medicine

SUBMITTER: Li R 

PROVIDER: S-EPMC6748812 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature